We develop and apply multi-scale models of forest dynamics and management, landscape dynamics, and land-use change to project and evaluate landscape-level management strategies that improve the resilience of forests and agroforestry ecosystems in the face of global threats. Our goal is to ensure the sustainable provision of the ecosystem services.

The modeling tools are complemented by spatial optimization methodologies to help design resilient agroforestry landscapes, taking multiple spatial priorities and objectives into account as well as constraints related to experts, landowners, and other stakeholders. These optimization tools are at the cutting edge of methodological approaches, using both heuristic and exact models to balance the diverse objectives—including biodiversity conservation, and social and economic objectives, among others—that often shape the decision-making process at the landscape level.

That is why we integrate remote sensing (e.g. satellite, LiDAR) and forest inventory data to produce detailed and up-to-date characterizations of forest conditions, develop online platforms to visualize the future evolution of forests and landscapes, and create applications to collect citizen science data that validates forest and fuel models.